New project compares models of climate’s impact on crops, water, more

New project aims to guide researchers and decision-makers.

One way that climate models are tested and improved is through a “model intercomparison project”. A number of modeling groups get together and use a collection of models to run the exact same simulations with the exact same inputs to see how the results line up. This can be done to generate collective projections of temperature change or just to look for parts of individual models that need work. Projects like these have been organized for a number of different types of models, but a new one examines models of climate change impacts.

A pile of papers published in the Proceedings of the National Academy of Sciences present the first fruits of the Inter-Sectoral Impact Model Intercomparison Project. This is the first time that models of things like water availability, crop yields, and malaria have been evaluated together. The organizers hope the project will not only provide the best available information to folks planning for (and dealing with) the impacts of climate change, but also help guide the research community towards the questions most in need of answers.

One of the studies, from a large team led by Jacob Schewe at the Potsdam Institute for Climate Impact Research, used models of Earth’s water cycle to examine the availability of the liquid in a warming world. Because warmer temperatures result in more evaporation and more intense precipitation, arid regions are generally expected to get drier, and wet ones wetter. Given that access to water is already a problem in many places, anything that exacerbates that situation is a step in the wrong direction.

Running eleven water cycle models with future warming simulated by five climate models, the researchers tracked how surface runoff replenishes stream flow, and used that as a measure of the amount of water available for human use. They compared changes after 1°C, 2°C, and 3°C increases in global average surface temperature following a “business-as-usual” greenhouse gas emissions trajectory. Since the other major determinant of water availability is the number of people needing that water, they also used a middling scenario of population growth in which the global population tops out at 10 billion around 2090.

The researchers calculated the volume of water available for each person by country, with volumes below 500 cubic meters per year categorized as “absolute water scarcity.” Applied to today’s conditions, that category would include less than two percent of the world’s population.

After 1°C of warming (and rising population), that increased to roughly six percent of the world’s population— nine percent at 2 °C and 12 percent at 3 °C. The majority of that increase is due to population growth, but warming adds about two to three percent to each number.

Those are the average numbers, but the results from each of the models covered a pretty broad range—from about one to 15 percent for the 1°C warming case, for example. This had much more to do with differences among the water cycle models than differences among the climate models, meaning that there’s significant room for improvement on those water cycle models.

Another study, this one led by NASA Goddard’s Cynthia Rosenzweig, works with models of crop yields around the world. These models simulate plant biology, including the timing of spring growth, the way roots respond to soil moisture conditions, and temperature effects on growth, as well as soil nutrient cycles. Seven of these models were again run using five climate models, but this time for four of the greenhouse gas emissions scenarios used in the last IPCC report.

Generally, the models showed lower crop yields in low latitude regions as the Earth warms, higher yields at high latitudes, and a smaller, less certain impact at mid-latitudes. Rising atmospheric CO2 concentrations have a beneficial effect on most crops (corn being one exception), but that effect shrinks when nutrient availability is accounted for. In other words, CO2 isn't the only thing that's limiting crop growth in most cases.

Here, again, the models produce a wide range of projections. There’s a great deal of uncertainty in the crop models, and the exact degree to which CO2 will boost crop growth in real world conditions is a major contributor to that uncertainty. The soils in far northern regions that could become climatically available for agriculture are also poorly explored, so it’s hard to know just how productive they could be. Additional factors like crop pests, soil degradation, and technological progress make it even more difficult to forecast agricultural production.

Uncertainty is a major theme running through this initial batch of papers, and not just because it’s an ever present fly in the ointment of science. By uncovering the factors that give these models the hardest time, this project helps point the way forward to better forecasts of the impacts we can expect from climate change. On the flip side, of course, it also highlights the points on which the various models agree.

In a colorful introduction to the papers, Hans Joachim Schellnhuber, Katja Frieler, and Pavel Kabat liken the body of climate impacts research to the well-known John Godfrey Saxe poem, “The Blind Men and the Elephant”. Each blind man, feeling only one part of the elephant, comes to a conflicting judgment—declaring the object to be a tree or a snake or a wall.

“The impressive body of expertise in the response of individual biophysical systems, and in turn of society, to the pressures of climate change must not be amalgamated to understand how our Earth and human system as a whole will respond,” the authors write. “It is time to put our knowledge of the legs, tusks, tail, and ears of the elephant together to understand the true nature of the beast.”

If taken at face value, this study only reinforces the need for a rapid buildout of next-gen nuclear. There is no evidence that world CO2 emissions will moderate anytime soon, despite the US leading the way on CO2 reduction. In fact, they are likely to continue at around the current rate for decades. So, if the world is facing a water crisis, nuclear desalination looks like the only affordable way out.

Besides - cheap, plentiful energy is desirable for a whole lot of other reasons.

Earth surface modeling is a noble endeavor. It is one of the frontiers of human exploration.

A lot of people don't quite get modeling, it seems. You get these guys who focus on not getting an accurate year-to-year prediction over a period of ten years. That was never how these things were supposed to work.

It's about probing for feedbacks, it's non-linear: to think you are going to get a perfectly accurate forecast of what happens in 2020 is foolish.

We know there are all kinds of problems in the fine detail. Measurement is hard, computationally it is incredibly hard, and then you have chaos in there too.

But you can work out the big picture. And if you keep at it, every year it gets more detailed.

If you understand the models, and work out what direction they are pointing in, then you will find out that there are big dangers in runaway climate change.

But models will always be secondary to direct evidence like what is happening in the Arctic.

I once thought that nuclear energy was the answer to many environmental problems, but after the Russian disaster and now the Japanese one, it just creates other problems. There is plenty of water on the earth, man will find a way to get it to the places that need it. As to the models only time will tell if there is any truth in them. One thing the models tend to ignore is Sun activity which can really bork them.

And yet they fail to actually test these models against the historical record to see if they match real world results. What difference does it make if they all give the same incorrect answer? It seems that these computer modelers don't care if their models apply to the real world all that much. Why is that? Most programs are tested against the real world, why not climate models? But then, if they failed, it would bring their entire industry under the microscope. And that they cannot stand for!

And yet they fail to actually test these models against the historical record to see if they match real world results. What difference does it make if they all give the same incorrect answer? It seems that these computer modelers don't care if their models apply to the real world all that much. Why is that? Most programs are tested against the real world, why not climate models? But then, if they failed, it would bring their entire industry under the microscope. And that they cannot stand for!

Models are tested against the record all of the time. Take off your Koch-sponsored blinkers, and remember silence may be mistaken for stupidity, but asinine braying is never mistaken for what it is.

And yet they fail to actually test these models against the historical record to see if they match real world results. What difference does it make if they all give the same incorrect answer? It seems that these computer modelers don't care if their models apply to the real world all that much. Why is that? Most programs are tested against the real world, why not climate models? But then, if they failed, it would bring their entire industry under the microscope. And that they cannot stand for!

And yet they fail to actually test these models against the historical record to see if they match real world results. What difference does it make if they all give the same incorrect answer? It seems that these computer modelers don't care if their models apply to the real world all that much. Why is that? Most programs are tested against the real world, why not climate models? But then, if they failed, it would bring their entire industry under the microscope. And that they cannot stand for!

And yet they fail to actually test these models against the historical record to see if they match real world results. What difference does it make if they all give the same incorrect answer? It seems that these computer modelers don't care if their models apply to the real world all that much. Why is that? Most programs are tested against the real world, why not climate models? But then, if they failed, it would bring their entire industry under the microscope. And that they cannot stand for!